Asbestos Exposure and Leukemia Incidence: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background/Aim: Asbestos comprises six naturally occurring fibrous minerals known for their health risks, particularly in occupational settings. This systematic review evaluates the association between asbestos exposure and leukemia incidence, synthesizing findings from various studies. Materials and Methods: We conducted a comprehensive literature search in PubMed, Cochrane Library, and Web of Science, adhering to PRISMA guidelines. Studies included participants exposed to asbestos compared to matched controls, focusing on leukemia incidence. Data extraction and quality assessment were performed using the Newcastle-Ottawa Scale. Results: A total of 1,751,580 participants were included, with 257,572 (14.7%) exposed to asbestos. The incidence of leukemia varied across studies, ranging from 0.02% to 0.45%. Meta-analysis revealed an overall risk ratio of 1.25 (95% confidence interval=0.81-1.94) with significant heterogeneity (I2=86%), indicating no statistically significant difference between exposed and non-exposed cohorts. Conclusion: The findings highlight the complexity of the relationship between asbestos exposure and leukemia, influenced by factors such as exposure type, duration, and confounding variables like smoking. While some studies suggest a potential link, the evidence remains inconclusive, necessitating further research. This review underscores the need for high-quality studies to clarify the association between asbestos and leukemia, informing public health policies to reduce exposure risks and protect vulnerable populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".